Matt Trainum recalls a recent conversation with a college leader that captured what’s been a staggering crescendo of investments in AI hiring.
The private institution that the leader runs is “well-heeled,” “well-funded,” and enjoys “an amazing reputation” with a Top 20 U.S. News ranking, says Trainum, vice president for networks and strategic partnerships at the Council of Independent Colleges. But even then, “He said, ‘There is no way we can keep up with the top five institutions. ... We are being left in the dust.’”
Competition for talent is common in higher ed. So is the reflex to bulk up the faculty in response to technological advances (look at, say, nanotechnology and genome science at the turn of the century). But the sense of urgency, and the scale of recent hiring initiatives in artificial intelligence, is noticeably amplified, more than a dozen college provosts, vice presidents, directors, and deans told The Chronicle.
In just one snapshot of the growing demand for expertise, an analysis of The Chronicle’s jobs site conducted earlier this year found that the number of AI-related listings had more than doubled between 2022 and 2023.
College leaders say there’s too much at stake not to build AI expertise into their teaching and research enterprises. On the research side, competitive grants are on the line, as are long-term aspirations for membership in the prestigious Association of American Universities, or an upgraded Carnegie research classification. Colleges recognize, too, that research across disciplines might fall behind without an infusion of AI experts. On the teaching side, filling classrooms with these experts can help cement one’s reputation as a college that can prepare students for a work force that’s rapidly embracing AI skills. It can help attract more learners.
But the competition for that talent is stiff. Expertise in artificial intelligence is suddenly a coveted asset across industries, and companies with deep pockets are similarly keen to hire. Last year, just 25 percent of more than 400 new Ph.D. candidates in AI/machine learning went into academe, according to the Computing Research Association’s 2023 Taulbee Survey. And those newly minted faculty members — along with those already in higher ed — are seeing wider interest in their talents as artificial intelligence becomes an increasingly interdisciplinary field.
As Martial Hebert, dean of the School of Computer Science at Carnegie Mellon University, puts it: They have “more opportunities.”
The reality on the ground, then, is an array of ambitious and creative plays to recruit. A small cohort of colleges have embarked on hiring sprees, snapping up hundreds of new graduates and courting faculty members from peer institutions. Some are erecting new AI-focused institutes and centers. And even at colleges where new hires may not be in the budget, the eagerness to advance teaching in the field has sparked training programs, and incentives for existing faculty members to experiment with AI technologies.
“You can’t just say, ‘We’re going to make a choice that we’re going to let other people deal with the AI question,’” Hebert says. “It’s not a choice.”
Just a few years ago, robust AI hiring — think of the University of Florida’s commitment in 2020 to hire 100 new AI-focused faculty members — was a niche, if not unique, endeavor.
Now others are making their own big bets. Purdue University and the University of Wisconsin at Madison have both announced about 50 new positions. At Emory University, up to 60. At the University of Georgia, 70. At the Johns Hopkins University, some 110. And that’s just accounting for hiring tied to an explicit university-backed initiative.
Funding is coming from a combination of sources. Examples colleges cited include multimillion-dollar donor gifts, investment “matches” from individual departments and central offices, and one-time “strategic” funds from administrations to cover startup costs for things like equipment and lab space.
The vision behind these investments, though, is often similar: to embed faculty with AI expertise across an institution, in subject areas that align with, or complement, an institution’s strengths.
Purdue, for example, isn’t focusing on AI in medicine since it doesn’t have a medical school, says Patrick Wolfe, the provost. But as a public college in a farming state, it makes sense to focus on areas like digital agriculture, and the use of AI to analyze data from drone photos, sensors in the soil, and other technologies to inform more efficient farming practices. (The university plans to hire 50 faculty to staff its new Institute for Physical AI.)
Wolfe’s example gets at another growing strategy, which is to seek out recruits who are focused on applications for AI. Faculty members who aren’t just developing, say, new algorithms or large-language models, but who are passionate about how AI can be used to solve societal challenges.
A lot of that type of hiring is happening in designated “clusters.” The University of Georgia has recruited nearly 70 faculty members in three years across 10 areas, as part of its Presidential Interdisciplinary Faculty Hiring Initiative in Data Science and AI. Recruits in one of those clusters, for example, are working on how to use AI to model disease transmission. Nearby, at Emory University, the Law and Social Justice cluster — one of four general-focus areas of the college’s AI.Humanity initiative — is looking at impacts on privacy and hiring bias.
If you ask UGA’s provost, Jack Hu, incorporating a focus on AI applications is what gives colleges without the computer-science muscle of an MIT or a Stanford a chance to make a mark, and be competitive.
Even so, leaders say it can be challenging to win over prospective hires. Some, especially Ph.D. candidates and postdoctoral fellows, can come to the table with half a dozen job offers in-hand, says Wolfe, at Purdue.
Graphic reflects hiring as of August 2024. Prior institutional affiliations were determined through a review of faculty members’ LinkedIn profiles and/or resumes.
So what’s the strategy?
A new position can be attractive because it offers professional growth. In cases where colleges have a new center or institute, they pitch job candidates on the chance to get in early and influence its development. Or there may be internal seed funding up for grabs — up to $150,000 per grant proposal at the University of Georgia, for example — to jump-start a new research venture.
Every good university is out there looking to poach talent.
A job may appeal because it means gaining passionate, like-minded colleagues — not just in one department, but around the university. There are “people trained in AI and computing who don’t necessarily want to hang out in a computing department,” says Ravi V. Bellamkonda, Emory’s provost. They’re interested “in being part of a community.”
Colleges often promise flexibility, too, such as joint appointments, altered teaching obligations and workloads, and crossover work with industry. (One Emory hire, for example, is taking a year off for a visiting fellowship at Google, a spokesperson confirmed. Another hire at the University of Southern California, which last year announced a $1-billion computing initiative, is the CEO of an AI research and product company.)
That industry piece is one Wolfe at Purdue thinks about a good deal. “How do we turn the attractions of industry into a strength for academic institutions, and not just a weakness?” he asks. “How do we find ways of working together?”
There’s also the longstanding business strategy that some don’t like to talk about publicly: poaching.
“Every good university is out there looking to poach talent,” Wolfe says. His institution, in fact, even set up a new universitywide program over the last 18 months — “Movable Dream Hires” — where Purdue faculty members essentially approach peers at other colleges and say, “‘My department thinks you’d be really great if we could bring you to Purdue. Would you be interested?’” Wolfe explains.
(The Chronicle asked college leaders if they worry about retention. Everyone gave a resounding yes, noting how many of their recruitment strategies also apply to keeping existing talent in-house.)
Recruitment and retention aren’t the only considerations. Hiring at this scale, and in this field, raises questions that many leaders are contending with for the first time.
Some have to do with logistics, and capacity. Will everyone have the lab space they need? Does the university have access to enough computational power? The more people you have on campus developing and training AI models, analyzing large datasets, and running AI tools for various applications, the more power is required.
For Hu at UGA, that reality has meant investing about $8.1 million “in high-performance computing, AI computing, and storage and data management resources” between 2022 and 2024, compared to $3.1 million the three years prior.
Charles Isbell Jr., provost at the University of Wisconsin at Madison, is also thinking about what the mass injection of AI experts will mean for the university’s educational and business operations. On top of the up to 50 new hires tied to the institution’s RISE-AI initiative, nearly 100 other AI-related faculty members are expected to join the ranks in the five-year period between 2022 and 2027.
For example, “How is that going to impact the way we do student advising?” Isbell asks. “I don’t know how many of us have reflected deeply about how bringing these kinds of people in across all of these disciplines is going to actually impact what we do.”
Not conducting coordinated large-scale hiring sprees doesn’t necessarily signal a lack of interest in AI recruitment, of course.
In some (albeit rarer) cases, it’s because an institution already has a robust Rolodex of experts. At Carnegie Mellon, for example, Hebert says there are 130 AI-related faculty in the School of Computer Science alone.
The more common practice, though, considering financial constraints, is to hire on a more case-by-case basis at the department level, using strategies such as filling vacated roles with AI experts. This is the case at Clemson University, a public land-grant research institution in South Carolina. Bob Jones, the provost, estimates Clemson has made 55 AI-related hires since 2022 across all departments.
When departments at Clemson hire, a tendency to date has been to hire early-career assistant professors and “see what develops,” Jones says. If that assistant professor becomes a “star” hire — perhaps securing multiple research grants, or acting as a “major adviser” to a large number of graduate students and postdocs — that can create a case for additional investments to build out a team around them. That’s what happened, Jones adds, with Nathan McNeese, who became the founding director of Clemson’s new Center for Human-AI Interaction, Collaboration, and Teaming, announced earlier this year. The center intends to conduct interdisciplinary research on how humans and AI can coexist.
Still, Jones is candid that recruiting is tough. The job-posting success rate for junior-level hires in particular, while still “quite good” at about 75 percent, is 5- to 10-percent lower than average. So recruiters often gather intel on candidates of interest ahead of time, looking for a plug. Do they have family nearby? Do we know if they’re looking to move?
Jones never underestimates the power of perks: A lower cost of living. Good school systems. Better weather. Places to ride your horse … if you happen to have one.
“We’re all humans,” he says. “Even rockstar faculty have lives.”
Miami Dade College, in Florida, is similarly banking on faculty members who are drawn to its doors for something other than premier research status.
The community college started the state’s first associate degree in Applied AI last year. And starting this fall, students can work toward a complementing bachelor’s degree. That requires more teaching professors — especially given the program’s burgeoning popularity, says Antonio Delgado, the vice president for innovation and technology partnerships. (Nearly 200 students enrolled in 2023-24, Delgado notes — an “unthinkable” number compared with the 20 to 30 students who typically join a new program in its first year. He expects that a similar number will enroll in just the fall of 2024 semester alone, with an additional 25 moving on to the bachelor’s program.)
To meet some of the need, Miami Dade has been able to leverage a $15-million investment from local government and the Knight Foundation that provides for up to 10 full-time AI-focused faculty, and five visiting faculty over the six-year grant.
Even so, “what we can pay based on our existing appropriations is not competitive” with many other universities, Delgado says. A recent posting for an AI-focused faculty member — at least two years of teaching experience desired — was $53,274 to $65,370. While not an apples-to-apples comparison, the 2023 Taulbee survey reported salaries ranging from $75,000 to more than $124,000 for early-career teaching faculty in computer-science departments at public colleges.
Delgado is still looking to fill two remaining full-time spots, as well as the visiting-faculty positions. To help bridge the teaching gap, he’s relying on part-time adjuncts — 10 to 15 of them at the time of reporting for this story.
The follow-up question seemed unavoidable: What if you can’t meet the demand?
Delgado politely dismisses the idea. “That’s not even an option we consider,” he says. “We just continue posting.”
A sense of obligation to produce an AI-informed citizenry is driving many colleges — namely those struggling to hire or unable to fund new positions — to identify alternative ways of honing talent.
One avenue is to promote an existing faculty member with AI expertise, if budgeting allows. At the Community College of Beaver County, in Pennsylvania, Thomas Peyton, an assistant professor of mathematics, became the college’s technical faculty lead for artificial intelligence after the college received nearly $800,000 as part of a grant through the federal Build Back Better Regional Challenge. The role change meant Peyton now works year-round, and teaches two classes per semester, instead of five.
AI is so pervasive in everything that we do.
At Thomas College, a small, riverside institution in central Maine, AI responsibilities are being integrated into existing roles. The academic-support staffer in the library, for example, now works with interested faculty members one-on-one to identify AI resources such as course materials, scholarly papers, and potential workshops, says Chris Rhoda, vice president for information services and chief information officer. Other colleges have created “innovation funds” — in Miami Dade’s case, up to $10,000 for project winners — to encourage faculty members to experiment with AI.
Teaching existing faculty AI skills, largely with the support of external entities, has emerged as a key strategy. Many colleges have collaborated with companies including Intel and Amazon Web Services, which provide resources like sample curricula, course content, and trainings. Member organizations are also creating programming; for example, the Council of Independent Colleges’s AI Ready Network is, among other things, helping campuses run generative-AI projects, such as building small chatbots for internal purposes or performing exploratory data analysis on a specific topic, Trainum wrote in an email. Miami Dade itself has become a professional-development hub, tapping a nearly $3-million grant from the National Science Foundation to head the new National Applied AI Consortium, in partnership with the Maricopa County Community College District and Houston Community College.
If you ask Rhoda, that approach isn’t a Plan B. It’s necessary. “AI is so pervasive in everything that we do,” so “for us to have the majority of our faculty and staff learning new things, discussing what they’re learning, helping each other build skills … that’s going to have an institutional impact,” he says.
Networking and continued-learning opportunities help keep Peyton energized about the work. He’s one of the few AI aficionados at his institution — the third smallest community college in Pennsylvania, with fewer than 2,000 students. He’s enjoyed attending conferences and meet-ups, like a five-day workshop that Carnegie Mellon held last month through its AI Institute for Societal Decision Making. For those few days, he soaked up all he could on topics like the social implications of AI, and came back with various interactive resources on AI fundamentals for his colleagues and students.
Does he ever wish he worked at one of those institutions who’ve invested heavily in AI?
“I do like to be surrounded by like-minded people,” he says. But he also likes the challenge of bringing AI into his colleagues’ worlds. So he’s staying put for now.
“You get your batteries charged back up” at those kinds of events, he says. “Then you come back and fight on.”
